\(N\)  sub-sequences will be generated after the CEEMDAN decomposition. Directly using them as the input data of the prediction model GWO-LSTM will cause a large computational cost. Therefore, we employ SE (sample entropy) [1]}, an approximate entropy (ApEn) [2]} method that evaluates time series complexities by measuring the probability of the new generated patterns, to classify and reconstruct the traffic flow temporal data for reducing the complexity of the sub-sequences. SE is defined as the negative natural logarithm of the conditional probability, where self-matches are not included:
\(\text{SampEn}(m, r)=\lim _{N \rightarrow \infty }\left\lbrace -\ln \left[\frac{A^{m}(r)}{B^{m}(r)}\right]\right\rbrace \) 
